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Abstract
In this work, multivariate statistical analysis (MVA) techniques are coupled with laser-induced breakdown spectroscopy (LIBS) to identify preservative types (chromated copper arsenate, ammoniacal copper zinc or alkaline copper quat), and to predict elemental content in preservative-treated wood. The elemental composition of the samples was measured with a standard laboratory method of digestion followed by atomic absorption spectroscopy analysis. The elemental composition was then correlated with the LIBS spectra using projection to latent structures (PLS) models. The correlations for the different elements introduced by different treatments were very strong, with the correlation coefficients generally above 0.9. Additionally, principal component analysis (PCA) was used to differentiate the samples treated with different preservative formulations. The research has focused not only on demonstrating the application of LIBS as a tool for use in the forest products industry, but also considered sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis of this complex wood substrate.
Published Version
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